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检索条件"任意字段=Conference on Visual Communications and Image Processing 2001"

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Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Och, Hannah Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany Leipzig Univ Telecommun Deutsch Telekom AG Inst Commun Engn Gustav Freytag Str 43-45 D-04277 Leipzig Germany

ISBN: (纸本)9781728185514

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： distribution modelling lossless coding screen content coding color image compression soft context formation

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image Retrieval Method using visual Query Suggestion and Relevance Feedback

Image Retrieval Method using Visual Query Suggestion and Rel...

引用

4th International conference on Wireless communications and Signal processing (WCSP)

作者： Zhang, Jing Yang, Yuncong Zhuo, Li Diao, Mengmeng Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

ISBN: (纸本)9781467358293;9781467358309

Query suggestion is an effective human-computer interaction (HCI) approach in information retrieval. According to human vision system, the image retrieval method using visual query suggestion (VQS) can provide a friendly query interface to solve the query ambiguity problem. In this paper, the contentbased image retrieval (CBIR) system is realized first. Human-computer interaction used VQS to obtain users' intention. In this step, user submitted a query keyword to the system. VQS is utilized to provide a list of suggestions, each containing a keyword and a collection of representative images. If the user selects one of the image suggestions, this image will be viewed as the key image. Then CBIR system will retrieve the image sets to return the similar images based on the similarity of image features. In relevance feedback, user scored each returned image by the slider to optimize retrieval results. A friendly query interface is designed to carry out HCI in our system and the experimental result shows the proposed method can improve the average recall and precision efficiently.

关键词： human-computer interaction visual query suggestion content-based image retrieval relevance feedback

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An Efficient image Retrieval Model with Convolutional Neural Network based Text/image Identification for Copyright Violation Detection 30

An Efficient Image Retrieval Model with Convolutional Neural...

引用

30th IEEE Signal processing and communications Applications conference (SIU)

作者： Ozden, Harun Tavli, Bulent Demirci, M. Fatih Anadolu Ajansu Iletisim Teknol Direktorlugu Ankara Turkey TOBB Ekon Teknol Univ Elekt Elekt Muhendisligi Ankara Turkey Ankara Yildirim Beyazit Univ Bilgisayar Muhendishgi B Ankara Turkey

ISBN: (纸本)9781665450928

One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database.

关键词： Bag of visual Words SIFT Convolutional Neural Network image Retrieval

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Joint compression-classification with quantizer/classifier dimension mismatch

Joint compression-classification with quantizer/classifier d...

引用

conference on visual communications and image processing 2001

作者： Srinivasamurthy, N Ortega, A Univ So Calif Dept EE Syst Integrated Media Syst Ctr Los Angeles CA 90089 USA

ISBN: (纸本)0819439886

In this paper an algorithm is presented to design, encoders that achieve good compression and classification. The goal is to minimize the classification error introduced by quantizing the data using encoders operating on low dimension, inputs, which are subsets of the high dimension data used by the classifier for classification. In the encoder design information from the other dimensions of the vector is used to develop efficient encoders which are capable of achieving lower classification error for a given distortion. The design allows a trade-off between distortion and classifications costs providing more flexibility in the overall system design. The algorithm is tested on Gaussian mixture data, which is classified using a classifier which takes as input vectors of quantized valves. Tile proposed technique can trade performance to achieve lower complexity, which is desirable in devices having limited computational resources. For 4 dimensional Gaussian mixture data the misclassification was about 2.2% more than, that achieved by using Encoders of the same dimension as the classifier, while the encoding complexity was reduced by more than a factor of 2.

关键词： image coding

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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OBJECTIVE QUALITY ASSESSMENT FOR image RETARGETING BASED ON PERCEPTUAL DISTORTION AND INFORMATION LOSS

OBJECTIVE QUALITY ASSESSMENT FOR IMAGE RETARGETING BASED ON ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Hsu, Chih-Chung Lin, Chia-Wen Fang, Yuming Lin, Weisi School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781479902880

image retargeting techniques aim to obtain retargeted images with different sizes or aspect ratios for various display screens. Various content-aware image retargeting algorithms have been proposed recently. However, there is still no accurate objective metric for visual quality assessment of retargeted images. In this paper, we propose a novel objective metric for assessing visual quality of retargeted images based on perceptual geometric distortion and information loss. The proposed metric measures the geometric distortion of retargeted images by SIFT flow variation. Furthermore, a visual saliency map is derived to characterize human perception of the geometric distortion. On the other hand, the information loss in a retargeted image, which is calculated based on the saliency map, is integrated into the proposed metric. A user study is conducted to evaluate the performance of the proposed metric. Experimental results show the consistency between the objective assessments from the proposed metric and subjective assessments.

关键词： image Retargeting SIFT Flow Quality Assessment Quality Evaluation

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A 360-degree panoramic camera using a mirror rotation mechanism

A 360-degree panoramic camera using a mirror rotation mechan...

引用

conference on visual communications and image processing 2001

作者： Nakao, T Kashitani, A NEC Corp Ltd Comp & Commun Media Res Nara 6300101 Japan

ISBN: (纸本)0819439886

In this paper, we describe a new panoramic camera that incorporates a 2-axes mirror rotation mechanism and image mosaicing software to get high-resolution panoramic images in a short time. The mirror is located in front of the camera via the mirror rotation mechanism to move the camera's view. Partial images taken with the mirror rotation are merged into a high-resolution panoramic image by mosaicing software. The 2-axes mirror rotation mechanism consists of a minor pedestal and a cam. The image mosaicing software projects each partial image onto a projection surface around the rotation center of the mirror. In that process, the projection center is shifted from the original principal point of the lens or the viewpoint to the mirror rotation center, and the projection positions for each pixel of partial images are calculated by using the limiting point. With these features, our panoramic camera has four advantages: 1) accurate, continuous, high-resolution, large and endless (360 degrees wide) panoramic images;2) short panoramic image acquisition time with fast mirror rotation;3) small body size;and 4) endless (360 degrees wide) fast mirror rotation. We also describe its prototype and its application to the Internet broadcasting of a tennis game.

关键词： panoramic camera moving parallax image mosaicing 2-axes mirror rotation mechanism Internet broadcast

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A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

A Hybrid Model for Natural Face De-Identiation with Adjustab...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wen, Yunqian Liu, Bo Xie, Rong Zhu, Yunhui Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728180687

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, b ecomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing deidentified f ace i mages a ccording t o a s ingle i nput. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing deidentified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.

关键词： privacy protection multi-level features fusion GAN-based face generation randomness

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MULTI-MODEL PREDICTION FOR image SET COMPRESSION

MULTI-MODEL PREDICTION FOR IMAGE SET COMPRESSION

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Shi, Zhongbo Sun, Xiaoyan Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781479902880

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model prediction (MoP) method for image set compression to significantly reduce inter image redundancy. Unlike the previous prediction methods, our MoP enhances the correlation between images using feature-based geometric multi-model fitting. Based on estimated geometric models, multiple deformed prediction images are generated to reduce geometric distortions in different image regions. The block-based adaptive motion compensation is then adopted to further eliminate local variances. Experimental results demonstrate the advantage of our approach, especially for images with complicated scenes and geometric relationships.

关键词： image set compression multiple models prediction HEVC

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

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IEEE International conference on visual communications and image processing (VCIP)

作者： Zhong, Tingting Jin, Xin Tong, Kedeng Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728180687

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient S quare rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.

关键词： Plenoptic image compression Autoencoder network 3D-CNN

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